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Early mortality prediction in intensive care unit patients based on serum metabolomic

2024-12-19Journal article Open access
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dc.contributor.authorAraújo, Rúben
dc.contributor.authorRamalhete, Luís
dc.contributor.authorVon Rekowski, Cristiana
dc.contributor.authorFonseca, Tiago AH
dc.contributor.authorBento, Luís
dc.contributor.authorCalado, Cecília
dc.date.accessioned2025-01-07T08:15:50Z
dc.date.available2025-01-07T08:15:50Z
dc.date.issued2024-12-19
dc.description.abstractPredicting mortality in intensive care units (ICUs) is essential for timely interventions and efficient resource use, especially during pandemics like COVID-19, where high mortality persisted even after the state of emergency ended. Current mortality prediction methods remain limited, especially for critically ill ICU patients, due to their dynamic metabolic changes and heterogeneous pathophysiological processes. This study evaluated how the serum metabolomic fingerprint, acquired through Fourier-Transform Infrared (FTIR) spectroscopy, could support mortality prediction models in COVID-19 ICU patients. A preliminary univariate analysis of serum FTIR spectra revealed significant spectral differences between 21 discharged and 23 deceased patients; however, the most significant spectral bands did not yield high-performing predictive models. By applying a Fast Correlation-Based Filter (FCBF) for feature selection of the spectra, a set of spectral bands spanning a broader range of molecular functional groups was identified, which enabled Naïve Bayes models with AUCs of 0.79, 0.97, and 0.98 for the first 48 h of ICU admission, seven days prior, and the day of the outcome, respectively, which are, in turn, defined as either death or discharge from the ICU. These findings suggest FTIR spectroscopy as a rapid, economical, and minimally invasive diagnostic tool, but further validation is needed in larger, more diverse cohorts.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationAraújo R, Ramalhete L, Von Rekowski CP, Fonseca TAH, Bento L, R. C. Calado C. Early Mortality Prediction in Intensive Care Unit Patients Based on Serum Metabolomic Fingerprint. International Journal of Molecular Sciences. 2024; 25(24):13609. https://doi.org/10.3390/ijms252413609pt_PT
dc.identifier.doi10.3390/ijms252413609pt_PT
dc.identifier.eissn1422-0067
dc.identifier.issn1661-6596
dc.identifier.urihttp://hdl.handle.net/10400.21/18120
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relation2024.02043.BD - FCTpt_PT
dc.relation2023.01951.BD - FCTpt_PT
dc.relationDiagnosis and prognosis disease biomarkers on critically ill patients with COVID towards a precision medicine – a machine learning approach
dc.relation.publisherversionhttps://www.mdpi.com/1422-0067/25/24/13609pt_PT
dc.subjectICU mortality predictionpt_PT
dc.subjectserum biomarkerspt_PT
dc.subjectFTIR spectroscopypt_PT
dc.subjectomicspt_PT
dc.titleEarly mortality prediction in intensive care unit patients based on serum metabolomicpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleDiagnosis and prognosis disease biomarkers on critically ill patients with COVID towards a precision medicine – a machine learning approach
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FDS%2F0117%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/OE/2021.05553.BD/PT
oaire.citation.endPage20pt_PT
oaire.citation.issue24pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleInternational Journal of Molecular Sciencespt_PT
oaire.citation.volume25pt_PT
oaire.fundingStream3599-PPCDT
oaire.fundingStreamOE
person.familyNameAraújo
person.familyNameRamalhete
person.familyNameVon Rekowski
person.familyNameHenrique Fonseca
person.familyNameBento
person.familyNameCalado
person.givenNameRúben Alexandre Dinis
person.givenNameLuís
person.givenNameCristiana
person.givenNameTiago Alexandre
person.givenNameLuís
person.givenNameCecília
person.identifier2296066
person.identifier1960990
person.identifier130332
person.identifier.ciencia-id9A18-BFDC-ED95
person.identifier.ciencia-idDF19-022D-AA10
person.identifier.ciencia-id8F1D-1D48-8551
person.identifier.ciencia-idE711-FA12-4784
person.identifier.ciencia-id9418-E320-3177
person.identifier.orcid0000-0002-9369-6486
person.identifier.orcid0000-0002-8911-3380
person.identifier.orcid0009-0009-6843-1935
person.identifier.orcid0000-0003-0741-2211
person.identifier.orcid0000-0002-0260-003X
person.identifier.orcid0000-0002-5264-9755
person.identifier.ridL-6623-2018
person.identifier.ridE-2102-2014
person.identifier.scopus-author-id57208672678
person.identifier.scopus-author-id6603163260
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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